Video services such as video on demand, broadcast video, etc. may be provided to customers via a variety of different kinds of video distribution networks. One factor in determining the quality of a video signal provided to a customer is the quality of the video feed supplied to the head end of a video distribution network. A video feed may be supplied to a head end by satellite, and therefore is subject to the quality of satellite communications, and further may be subject to natural phenomena such as solar flares, depending on the location of a satellite and the location of a head end. Further, in certain kinds of video distribution networks, such as a digital optical network, network congestion can adversely affect video quality.
Moreover, a video distribution network used to provide video services generally includes a number of different components that may affect video quality. For example, after entering a video distribution network at a head end, a video signal may traverse a number of different links and switches, and in some cases is converted from an analog signal to a digital signal, or vice versa. Moreover, the configuration of components in a video distribution network may affect the quality of video received at customer premises. For example, a digital switch may be configured to fail over to another switch when a bit error rate exceeds a predetermined threshold, although the quality of video that is received by the customer may be affected before the predetermined threshold is reached. Similarly, it will be understood that the carrier-to-noise-ratio allowed in a video distribution network may affect the quality of video that may be received by a customer.
Unfortunately, it is presently difficult to determine the quality of video actually received at customer premises. Certain methods that purport to directly measure video quality at a customer premises in fact directly measure quality at some point in a video distribution network upstream of the customer premises, and then apply mathematical techniques to predict, rather than actually measure, video quality at a customer premises. At best, only indirect indicators of signal quality, such as signal-to-noise ratio, carrier-to-noise ratio, signal level, etc. may presently be measured at a customer premises.
For example, various performance metrics are identified in ITU-T Prepublished Recommendation G.1050 “Network model for evaluating multimedia transmission performance over internet protocol,” published by the International Telecommunications Union (ITU) of Geneva Switzerland in November 2005, and fully incorporated herein by reference in its entirety. Such performance metrics include one-way latency, “jitter” (variability in latency), duration of sequential packet loss (“burst”), rate (frequency) of sequential packet loss, proportion of packets lost, and proportion of reordered packets, etc. Some vendors sell equipment for measuring certain of these metrics, e.g., Spirent® Communications, of West Sussex, United Kingdom.
However, known performance metrics are unfortunately at best imperfect predictors of the subjective quality of the output video as viewed by human observers. Full-reference video quality metrics (FR-VQM), e.g., metrics that directly measure video quality are generally thought to be better predictors of subjective quality. However, while systems and methods for correlating network performance metrics and FR-VQM are generally known and include graphical “scatter plots” and computed Pearson and Spearman correlation coefficients, present systems and methods do not provide any way to correlate network performance metrics, such as mentioned above, and high quality full-reference metrics in a video network because present systems and methods provide for no way in which to acquire and evaluate high quality video that has gone end-to-end, i.e., from a head end, to a customer premises equipment, and back. Having both a full-reference metric and network performance metrics would be quite valuable because a full-reference metric gives a more reliable indicator of subjective quality of the output video, while network performance metrics provide more useful diagnostic information for identifying and addressing network problems.